Large-Scale Hyperspectral Image Clustering Using Contrastive Learning

by   Yaoming Cai, et al.

Clustering of hyperspectral images is a fundamental but challenging task. The recent development of hyperspectral image clustering has evolved from shallow models to deep and achieved promising results in many benchmark datasets. However, their poor scalability, robustness, and generalization ability, mainly resulting from their offline clustering scenarios, greatly limit their application to large-scale hyperspectral data. To circumvent these problems, we present a scalable deep online clustering model, named Spectral-Spatial Contrastive Clustering (SSCC), based on self-supervised learning. Specifically, we exploit a symmetric twin neural network comprised of a projection head with a dimensionality of the cluster number to conduct dual contrastive learning from a spectral-spatial augmentation pool. We define the objective function by implicitly encouraging within-cluster similarity and reducing between-cluster redundancy. The resulting approach is trained in an end-to-end fashion by batch-wise optimization, making it robust in large-scale data and resulting in good generalization ability for unseen data. Extensive experiments on three hyperspectral image benchmarks demonstrate the effectiveness of our approach and show that we advance the state-of-the-art approaches by large margins.



There are no comments yet.


page 1

page 8


You Never Cluster Alone

Recent advances in self-supervised learning with instance-level contrast...

BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification

Deep learning based landcover classification algorithms have recently be...

Semi-supervised Hyperspectral Image Classification with Graph Clustering Convolutional Networks

Hyperspectral image classification (HIC) is an important but challenging...

Spatial-Spectral Clustering with Anchor Graph for Hyperspectral Image

Hyperspectral image (HSI) clustering, which aims at dividing hyperspectr...

Self-supervised Hyperspectral Image Restoration using Separable Image Prior

Supervised learning with a convolutional neural network is recognized as...

Domain Adaptor Networks for Hyperspectral Image Recognition

We consider the problem of adapting a network trained on three-channel c...

Fusion of heterogeneous bands and kernels in hyperspectral image processing

Hyperspectral imaging is a powerful technology that is plagued by large ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.